Spatial modeling of snow water equivalent using covariances estimated from spatial and geomorphic attributes
Spatial modeling of snow water equivalent using covariances estimated from spatial and geomorphic attributes
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DOI：
10.1016/s0022-1694(96)03062-4
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发表时间：
1997-03-01
影响因子：
6.4
通讯作者：
Cressie, N
中科院分区：
文献类型：
--
作者：
Carroll, SS;Cressie, N
由于美国的用水需求迅速接近可用供水总量，因此准确监测水资源至关重要。因此，国家气象局 (NWS) 维护着一套概念性、连续的水文模拟模型，用于生成扩展的水流预测、供水前景和洪水预报。为了获得准确的预测和预测，有必要在整个雪季定期估计美国各地河流流域的雪水当量。这些估计是使用地统计模型和雪道、SNOTEL 和机载雪数据获得的。在这项研究中，我们开发了一个正定空间协方差函数，使研究人员能够在获得雪水当量估计值时纳入地貌地点属性。我们使用在北福克克利尔沃特河流域收集的雪道和 SNOTEL 数据来说明我们的方法。我们的结果表明，通过将高程纳入北福克克利尔沃特河流域使用的协方差模型中，我们能够大幅提高雪水当量估计的准确性。
As the demand for water in the USA rapidly approaches the total available water supply, it is essential that water resources be accurately monitored. Consequently, the National Weather Service (NWS) maintains a set of conceptual, continuous, hydrologic simulation models used to generate extended streamflow predictions, water supply outlooks, and flood forecasts. To obtain accurate predictions and forecasts, it is necessary, periodically throughout the snow season, to estimate the snow water equivalent in river basins throughout the USA. The estimates are obtained using a geostatistical model and snow course, SNOTEL, and airborne snow data. in this research, we develop a positive-definite spatial covariance function that allows researchers to incorporate geomorphic site attributes when snow water equivalent estimates are obtained. We illustrate our approach using snow course and SNOTEL data collected in the North Fork Clearwater River basin. Our results indicate that by incorporating elevation into the covariance model used for the North Fork Clearwater River basin we are able to improve substantially the accuracy of the snow water equivalent estimates.